HateSense：用于仇恨言论检测的分层情感分析框架与二分类及多分类策略研究

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Abstract:Hate speech detectors must separate hateful posts from the far more common offensive but non-hateful ones, and two popular ideas for doing so are hierarchical (coarse-to-fine) classification and sentiment features. We revisit our earlier transformer-based framework, which combines both ideas, and test each component in a controlled study on two public benchmarks (Davidson et al., 24,783 tweets; HateXplain, 19,229 posts) with five random splits, validation-based tuning, and paired bootstrap tests. With linear models over word and character n-grams, the dominant-class-first hierarchy used in our framework lowered macro-F1 by 1.1-1.5 points relative to a flat classifier (p <= 0.007), and adding VADER sentiment features changed Davidson macro-F1 by at most 0.6 points, never significantly in the class-weighted settings. Class-weighted training was the only intervention with a large effect (+2.0 to +3.5 macro-F1 and +5.9 to +9.1 hate-class F1), and sentiment cannot separate the two abusive classes because hateful and offensive tweets are almost equally negative (mean VADER compound -0.35 vs. -0.29). Cross-dataset transfer cut macro-F1 to 43-53, and a reclaimed slur doubled the false hate rate of a logistic-regression detector, showing that in-domain scores overstate real-world reliability.

Comments:	10 Pages
Subjects:	Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as:	arXiv:2411.05819 [cs.CL]
	(or arXiv:2411.05819v2 [cs.CL] for this version)
	https://doi.org/10.48550/arXiv.2411.05819 arXiv-issued DOI via DataCite

Submission history

From: Shahran Rahman Alve [view email]
[v1] Sun, 3 Nov 2024 04:11:33 UTC (471 KB)
[v2] Tue, 27 Oct 2026 14:21:12 UTC (84 KB)